Jumpdetector -> JumpDetector & all_mins in JumpDetector.__predict

analytics/detectors/__init__.py

@@ -2,4 +2,4 @@ from detectors.general_detector import GeneralDetector
 from detectors.pattern_detector import PatternDetector
 from detectors.peaks_detector import PeaksDetector
 from detectors.step_detector import StepDetector
-from detectors.jump_detector import Jumpdetector
+from detectors.jump_detector import JumpDetector

analytics/detectors/general_detector/general_detector.py

@@ -10,7 +10,6 @@ import os.path
 import json
 
 
-
 NANOSECONDS_IN_MS = 1000000
 
 logger = logging.getLogger('analytic_toolset')

analytics/detectors/jump_detector.py

@@ -7,41 +7,13 @@ from scipy.signal import argrelextrema
 import math
 
 
-class Jumpdetector:
+class JumpDetector:
 
     def __init__(self):
         self.segments = []
         self.confidence = 1.5
         self.convolve_max = 120
     
-    def intersection_segment(self, data, median):
-        cen_ind = []
-        for i in range(1, len(data)-1):
-            if data[i - 1] < median and data[i + 1] > median:
-                cen_ind.append(i)
-        del_ind = []
-        for i in range(1,len(cen_ind)):
-            if cen_ind[i] == cen_ind[i - 1] + 1:
-                del_ind.append(i - 1)
-        del_ind = del_ind[::-1]
-        for i in del_ind:
-            del cen_ind[i]
-        return cen_ind
-
-    def logistic_sigmoid(self, x , y, alpha, height):
-        distribution = []
-        for i in range(x, y):
-            F = 1 * height / (1 + math.exp(-i * alpha))
-            distribution.append(F)
-        return distribution
-    
-    def alpha_finder(self, data):
-        """
-        поиск альфы для логистической сигмоиды
-        """
-        pass
-
-
     async def fit(self, dataframe, segments):
         #self.alpha_finder()
         data = dataframe['value']
@@ -64,10 +36,10 @@ class Jumpdetector:
                 min_line = ax_list[min_peak, 0]
                 max_line = ax_list[max_peak, 0]
                 sigm_heidht = max_line - min_line
-                pat_sigm = logistic_sigmoid(-120, 120, 1, sigm_heidht)
+                pat_sigm = utils.logistic_sigmoid(-120, 120, 1, sigm_heidht)
                 for i in range(0, len(pat_sigm)):
                     pat_sigm[i] = pat_sigm[i] + min_line 
-                cen_ind = self.intersection_segment(flat_segment, mids[0])
+                cen_ind = utils.intersection_segment(flat_segment, mids[0])
                 c = []
                 for i in range(len(cen_ind)):
                     x = cen_ind[i]
@@ -105,26 +77,20 @@ class Jumpdetector:
         else:
             self.convolve_max = 120 # макс метрика свертки равна отступу(120), вау!
     
-    def logistic_sigmoid(x1, x2, alpha, height):
-        distribution = []
-        for i in range(x, y):
-            F = 1 * height / (1 + math.exp(-i * alpha))
-            distribution.append(F)
-        return distribution
-    
-    def predict(self, dataframe):
+    async def predict(self, dataframe):
         data = dataframe['value']
 
-        result = self.__predict(data)
+        result = await self.__predict(data)
         result.sort()
 
         if len(self.segments) > 0:
             result = [segment for segment in result if not utils.is_intersect(segment, self.segments)]
         return result
 
-    def __predict(self, data):
+    async def __predict(self, data):
         window_size = 24
         all_max_flatten_data = data.rolling(window=window_size).mean()
+        all_mins = argrelextrema(np.array(all_max_flatten_data), np.less)[0]
         extrema_list = []
         # добавить все пересечения экспоненты со сглаженным графиком
         
@@ -172,3 +138,9 @@ class Jumpdetector:
                 (self.confidence, self.convolve_max) = pickle.load(file)
         except:
             pass
+
+    def alpha_finder(self, data):
+        """
+        поиск альфы для логистической сигмоиды
+        """
+        pass

analytics/detectors/pattern_detector.py

@@ -17,12 +17,12 @@ logger = logging.getLogger('analytic_toolset')
 
 
 def resolve_detector_by_pattern(pattern):
-    if pattern == "peak":
+    if pattern == 'peak':
         return detectors.PeaksDetector()
-    if pattern == "drop":
+    if pattern == 'drop':
         return detectors.StepDetector()
-    if pattern == "jump":
-        return detectors.Jumpdetector()
+    if pattern == 'jump':
+        return detectors.JumpDetector()
     raise ValueError('Unknown pattern "%s"' % pattern)
 
 

analytics/utils/__init__.py

@@ -59,3 +59,24 @@ def segments_box(segments):
     min_time = pd.to_datetime(min_time, unit='ms')
     max_time = pd.to_datetime(max_time, unit='ms')
     return min_time, max_time
+
+def intersection_segment(data, median):
+    cen_ind = []
+    for i in range(1, len(data)-1):
+        if data[i - 1] < median and data[i + 1] > median:
+            cen_ind.append(i)
+    del_ind = []
+    for i in range(1,len(cen_ind)):
+        if cen_ind[i] == cen_ind[i - 1] + 1:
+            del_ind.append(i - 1)
+    del_ind = del_ind[::-1]
+    for i in del_ind:
+        del cen_ind[i]
+    return cen_ind
+
+def logistic_sigmoid(self, x1, x2, alpha, height):
+    distribution = []
+    for i in range(x1, x2):
+        F = 1 * height / (1 + math.exp(-i * alpha))
+        distribution.append(F)
+    return distribution